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Toward Practical Secure Stable Matching

Toward Practical Secure Stable Matching Abstract The Stable Matching (SM) algorithm has been deployed in many real-world scenarios including the National Residency Matching Program (NRMP) and financial applications such as matching of suppliers and consumers in capital markets. Since these applications typically involve highly sensitive information such as the underlying preference lists, their current implementations rely on trusted third parties. This paper introduces the first provably secure and scalable implementation of SM based on Yao’s garbled circuit protocol and Oblivious RAM (ORAM). Our scheme can securely compute a stable match for 8k pairs four orders of magnitude faster than the previously best known method. We achieve this by introducing a compact and efficient sub-linear size circuit. We even further decrease the computation cost by three orders of magnitude by proposing a novel technique to avoid unnecessary iterations in the SM algorithm. We evaluate our implementation for several problem sizes and plan to publish it as open-source. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings on Privacy Enhancing Technologies de Gruyter

Toward Practical Secure Stable Matching

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Publisher
de Gruyter
Copyright
Copyright © 2017 by the
ISSN
2299-0984
eISSN
2299-0984
DOI
10.1515/popets-2017-0005
Publisher site
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Abstract

Abstract The Stable Matching (SM) algorithm has been deployed in many real-world scenarios including the National Residency Matching Program (NRMP) and financial applications such as matching of suppliers and consumers in capital markets. Since these applications typically involve highly sensitive information such as the underlying preference lists, their current implementations rely on trusted third parties. This paper introduces the first provably secure and scalable implementation of SM based on Yao’s garbled circuit protocol and Oblivious RAM (ORAM). Our scheme can securely compute a stable match for 8k pairs four orders of magnitude faster than the previously best known method. We achieve this by introducing a compact and efficient sub-linear size circuit. We even further decrease the computation cost by three orders of magnitude by proposing a novel technique to avoid unnecessary iterations in the SM algorithm. We evaluate our implementation for several problem sizes and plan to publish it as open-source.

Journal

Proceedings on Privacy Enhancing Technologies – de Gruyter

Published: Jan 1, 2017

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